1. Field of the Invention
The present invention relates to prediction and diagnostic techniques relating to the life span of a manufacturing apparatus using a rotary machine. In particular, it relates to a method for predicting the life span of a rotary machine such as a dry pump and a manufacturing apparatus including the rotary machine.
2. Description of the Related Art
Failure diagnosis has become important to ensure efficient semiconductor device manufacturing. In recent years, especially as the trend towards small volume production of many different items of system LSI increases, an efficient yet highly adaptable semiconductor device manufacturing method has become necessary. It is possible to use a plurality of small-scale production lines adapted for miscellaneous purposes in view of the efficient production of different semiconductor devices. However, if many small-scale production lines are constructed by merely miniaturizing large-scale production lines, investment efficiency may be reduced due to a decrease in the rate of manufacturing apparatus utilization. To rectify this situation, there is a method whereby different manufacturing processes are performed by one piece of manufacturing equipment. For example, in a LPCVD apparatus using a dry pump for the evacuation system, reactive gases and reaction products differ and formation situations for the reaction products within the dry pump differ depending on the type of manufacturing processes. Therefore, the manufacturing process affects the life of the dry pump.
If the dry pump should have a failure during a specific manufacturing process, then the lot products being processed will be defective. Moreover, excessive maintenance of the manufacturing apparatus may become necessary due to microscopic dust caused by residual reactive gases within the manufacturing apparatus. Implementation of such excessive maintenance causes the manufacturing efficiency of the semiconductor device to drop dramatically. If regular maintenance is scheduled with a margin of safety in order to prevent such sudden failures during the manufacturing process, the frequency of maintenance work on the dry pump may become astronomical. Not only does this increase maintenance costs, but also the decrease in availability of the semiconductor manufacturing apparatus is conspicuous due to changing the dry pump, which causes the manufacturing efficiency of the semiconductor device to decline sharply. In order to use a common semiconductor manufacturing apparatus for a plurality of processes, as is necessary for an efficient small-scale production line, it is desirable to accurately diagnose vacuum pump life and to operate the dry pump without having any waste in terms of time.
Previously, some methods of diagnosing dry pump life have been proposed. Basically, a state of the dry pump may be monitored by characteristics such as the motor current, vibration, and temperature, and methods have been provided to predict the pump life from changes in these characteristics. In particular, dry pump life span diagnosis methods have mainly been provided by monitoring the state of the dry pump through vibrations caused by the rotation of a rotor. Since a diagnosis using the vibration can be accomplished through measurements taken by merely attaching an accelerometer to a side of the dry pump, it has gained attention as a simple and easy method for predicting pump life span. In addition, as a method for predicting life span through measured vibration data, there has been proposed a method where deviation from a reference value for a high frequency component near 300 Hz is analyzed using neural networks (refer to Japanese Patent Application P2000-64964).
In the case of the technology disclosed in Japanese Patent Application P2000-64964, since a targeted frequency is high, changes accompanying pump operation, such as reaction product blockage may broaden the frequency spectrum, leading to a problem of decreased sensitivity.
In the case of calculating life prediction by monitoring transitions in a motor current of the dry pump, sensitive, accurate and stable life prediction is difficult because of variations in process conditions such as gas flow, or power supply.
Furthermore, when an accelerometer is attached to the dry pump, sensitivity changes depending on where and how it is attached, and a collection of highly sensitive and stable vibration data is difficult. Especially, the accelerometer is vulnerable to noise such as vibrations of other work in the vicinity of the semiconductor manufacturing apparatus, or changes of an inner pressure of the dry pump. Therefore, a variation of the observed vibration is desired to distinguish whether the observed or monitored vibration is a noise.